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Evaluating Black-Box Vulnerabilities with Wasserstein-Constrained Data Perturbations

Year: 2026

Type: conference-paper

Abstract: International audience

Source: IEEE Conference on Secure and Trustworthy Machine Learning (SaTML)

DOI: 10.1109/satml68715.2026.00010

Authors: Adriana Laurindo Monteiro, Loubes Jean-Michel

Institutions: Fundação Getulio Vargas, Institut national de recherche en sciences et technologies du numérique, Centre Inria de l'université de Bordeaux

Language: English

Topic: Adversarial Robustness in Machine Learning

Subfield: Artificial Intelligence

Field: Computer Science

Domain: Physical Sciences

Open Access status: green

OpenAlex ID: W7136100626

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